Metaheuristic Techniques for Test Case Generation: A Review

Meta Heuristic Search Based Techniques

A metaheuristic is a higher-level procedure to find a heuristic to provide a sufficiently good solution to an optimization problem, especially with incomplete or imperfect information or limited computation capacity (Harman et al., 2007). According to search strategies, it can be classified as local search and global search methods. Global algorithms make balance between exploration and exploitation of the search space by providing globally best solution. These algorithms terminate when a user defined stopping criteria is met. Unlike global search algorithm, local search algorithms do not give emphasis to exploration. They try to find a good solution among the neighbouring solutions. These algorithms terminate when they do not get any better neighbour solution and hence becomes locally optimal (Leonora et al., 2009) (see Figure 1).

Figure 1.

Search based methods

In this section, some most widely used local search algorithms like Hill Climbing (HC), Tabu Search (TS) and Simulated Annealing (SA) and global search algorithms like Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), Artificial Bee Colony (ABC) and Cuckoo Search (CS) are discussed. Also, their advantages and disadvantages and their application in suitable problems are mainly focused.